sklearndf.classification.wrapper.LinearDiscriminantAnalysisWrapperDF#

class sklearndf.classification.wrapper.LinearDiscriminantAnalysisWrapperDF(*args, **kwargs)[source]#

DF wrapper for sklearn.discriminant_analysis.LinearDiscriminantAnalysis.

Bases

NComponentsDimensionalityReductionWrapperDF [LinearDiscriminantAnalysis], ClassifierWrapperDF [LinearDiscriminantAnalysis]

Metaclasses

EstimatorWrapperDFMeta, ABCMeta

Parameters

args (Any) – transformer parameters
kwargs (Any) – transformer keyword parameters

Method summary

`clone`	Make an unfitted clone of this estimator.
`decision_function`	Compute the decision function for the given inputs.
`fit`	Fit this estimator using the given inputs.
`fit_transform`	Fit this transformer using the given inputs, then transform the inputs.
`from_fitted`	Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
`get_params`	Get the parameters for this estimator.
`inverse_transform`	Inverse-transform the given inputs.
`predict`	Predict outputs for the given inputs.
`predict_log_proba`	Predict class log-probabilities for the given inputs.
`predict_proba`	Predict class probabilities for the given inputs.
`score`	Score this learner using the given inputs and outputs.
`set_output`	See `sklearn.utils.set_output()`
`set_params`	Set the parameters of this estimator.
`to_expression`	Render this object as an expression.
`transform`	Transform the given inputs.

Attribute summary

`COL_FEATURE`	Name assigned to an `Index` or a `Series` with the names of the features used to fit a `EstimatorDF`.
`COL_FEATURE_ORIGINAL`	Name assigned to a `Series` with the original feature names before transformation.
`COL_PREDICTION`	Name of `Series` objects containing the predictions of single-output learners.
`classes_`	Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems
`feature_names_in_`	The pandas column index with the names of the features used to fit this estimator.
`feature_names_original_`	A pandas series, mapping the output features resulting from the transformation to the original input features.
`feature_names_out_`	A pandas column index with the names of the features produced by this transformer
`is_fitted`	`True` if this object is fitted, `False` otherwise.
`n_features_in_`	The number of features used to fit this estimator.
`n_outputs_`	The number of outputs used to fit this estimator.
`native_estimator`	The native estimator that this wrapper delegates to.
`output_names_`	The name(s) of the output(s) this supervised learner was fitted to.

Definitions

clone()#

Make an unfitted clone of this estimator.

Return type: LinearDiscriminantAnalysisWrapperDF
Returns: the unfitted clone

decision_function(X, **predict_params)#

Compute the decision function for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[Series, DataFrame]

Returns

a data frame with observations as rows and classes as columns, and values as the raw values predicted per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

fit(X, y=None, **fit_params)#

Fit this estimator using the given inputs.

Parameters

X (Union[DataFrame, Series]) – input data frame with observations as rows and features as columns
y (Union[Series, DataFrame, None]) – an optional series or data frame with one or more outputs
fit_params (Any) – additional keyword parameters as required by specific estimator implementations

Return type

LinearDiscriminantAnalysisWrapperDF

Returns

self

fit_transform(X, y=None, **fit_params)#

Fit this transformer using the given inputs, then transform the inputs.

Parameters

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
y (Optional[Series]) – an optional series or data frame with one or more outputs
fit_params (Any) – additional keyword parameters as required by specific transformer implementations

Return type

DataFrame

Returns

the transformed inputs

classmethod from_fitted(estimator, features_in, n_outputs)#

Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.

Parameters

estimator (LinearDiscriminantAnalysis) – the fitted native estimator to use as the delegate
features_in (Index) – the column names of X used for fitting the estimator
n_outputs (int) – the number of outputs in y used for fitting the estimator

Return type

LinearDiscriminantAnalysisWrapperDF

Returns

the wrapped data frame estimator

get_params(deep=True)#

Get the parameters for this estimator.

Parameters: deep (bool) – if True, return the parameters for this estimator, and for any sub-estimators contained in this estimator
Return type: Mapping[str, Any]
Returns: a mapping of parameter names to their values

inverse_transform(X)#

Inverse-transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters: X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
Return type: DataFrame
Returns: the reverse-transformed inputs

predict(X, **predict_params)#

Predict outputs for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[Series, DataFrame]

Returns

predictions per observation as a series, or as a data frame in case of multiple outputs

predict_log_proba(X, **predict_params)#

Predict class log-probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as log-probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

predict_proba(X, **predict_params)#

Predict class probabilities for the given inputs.

The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.

Parameters

X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
predict_params (Any) – optional keyword parameters as required by specific learner implementations

Return type

Union[DataFrame, List[DataFrame]]

Returns

a data frame with observations as rows and classes as columns, and values as probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output

score(X, y, sample_weight=None)#

Score this learner using the given inputs and outputs.

Parameters

X (Union[Series, DataFrame]) – data frame with observations as rows and features as columns
y (Series) – a series or data frame with the true outputs per observation
sample_weight (Optional[Series]) – optional series of scalar weights, for calculating the resulting score as the weighted mean of the scores for the individual predictions

Return type

float

Returns

the score

set_output(*, transform=None)#: See sklearn.utils.set_output()

set_params(**params)#

Set the parameters of this estimator.

Valid parameter keys can be obtained by calling get_params().

Parameters: params (Any) – the estimator parameters to set
Return type: LinearDiscriminantAnalysisWrapperDF
Returns: self

to_expression()#

Render this object as an expression.

Return type: Expression
Returns: the expression representing this object

transform(X)#

Transform the given inputs.

The inputs must have the same features as the inputs used to fit this transformer. The features can be provided in any order since they are identified by their column names.

Parameters: X (Union[Series, DataFrame]) – input data frame with observations as rows and features as columns
Return type: DataFrame
Returns: the transformed inputs

property classes_: Union[numpy.ndarray[Any, numpy.dtype[Any]], List[numpy.ndarray[Any, numpy.dtype[Any]]]]#

Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems

Raises: AttributeError – this classifier is not fitted
Return type: Union[ndarray[Any, dtype[Any]], List[ndarray[Any, dtype[Any]]]]

property feature_names_in_: pandas.Index#

The pandas column index with the names of the features used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: Index

property feature_names_original_: pandas.Series#

A pandas series, mapping the output features resulting from the transformation to the original input features.

The index of the resulting series consists of the names of the output features; the corresponding values are the names of the original input features.

Raises: AttributeError – this transformer is not fitted
Return type: Series

property feature_names_out_: pandas.Index#

A pandas column index with the names of the features produced by this transformer

Raises: AttributeError – this transformer is not fitted
Return type: Index

property is_fitted: bool#

True if this object is fitted, False otherwise.

Return type: bool

property n_features_in_: int#

The number of features used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: int

property n_outputs_: int#

The number of outputs used to fit this estimator.

Raises: AttributeError – this estimator is not fitted
Return type: int

property native_estimator: sklearndf.wrapper.T_NativeEstimator#

The native estimator that this wrapper delegates to.

Return type: TypeVar(T_NativeEstimator, bound= BaseEstimator)

property output_names_: List[str]#

The name(s) of the output(s) this supervised learner was fitted to.

Raises: sklearn.exceptions.NotFittedError – this estimator is not fitted
Return type: List[str]

sklearndf.classification.wrapper.ClassifierChainWrapperDF

sklearndf.classification.wrapper.MetaClassifierWrapperDF